Biogeography and organic matter removal shape long-term effects of timber harvesting on forest soil microbial communities
Bibliographic record
Abstract
The growing demand for renewable, carbon-neutral materials and energy is leading to intensified forest land-use. The long-term ecological challenges associated with maintaining soil fertility in managed forests are not yet known, in part due to the complexity of soil microbial communities and the heterogeneity of forest soils. This study determined the long-term effects of timber harvesting, accompanied by varied organic matter (OM) removal, on bacterial and fungal soil populations in 11- to 17-year-old reforested coniferous plantations at 18 sites across North America. Analysis of highly replicated 16 S rRNA gene and ITS region pyrotag libraries and shotgun metagenomes demonstrated consistent changes in microbial communities in harvested plots that included the expansion of desiccation- and heat-tolerant organisms and decline in diversity of ectomycorrhizal fungi. However, the majority of taxa, including the most abundant and cosmopolitan groups, were unaffected by harvesting. Shifts in microbial populations that corresponded to increased temperature and soil dryness were moderated by OM retention, which also selected for sub-populations of fungal decomposers. Biogeographical differences in the distribution of taxa as well as local edaphic and environmental conditions produced substantial variation in the effects of harvesting. This extensive molecular-based investigation of forest soil advances our understanding of forest disturbance and lays the foundation for monitoring long-term impacts of timber harvesting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".